Systematic review : prevalence of cannabis vaping
C. Lim
14/04/2021
PRISMA diagram
Lifetime + 30 day prevalence (products)
Prevalence by year
LT National + State surveys (U.S and Canada)
Prevalence by year 2013-16
Meta-analysis
## proportion 95%-CI %W(fixed) %W(random) byvar
## Florida Health,FYTS 0.0104 [0.0086; 0.0124] 1.8 3.6 9
## Florida Health,FYTS 0.0185 [0.0161; 0.0211] 3.3 3.6 9
## Florida Health,FYTS 0.0361 [0.0329; 0.0396] 6.5 3.7 9
## Florida Health,FYTS 0.0573 [0.0529; 0.0621] 8.3 3.7 9
## Florida Health,FYTS 0.0769 [0.0716; 0.0825] 10.1 3.7 10
## Florida Health,FYTS 0.0984 [0.0918; 0.1053] 10.3 3.7 11
## Florida Health,FYTS 0.1138 [0.1059; 0.1220] 9.3 3.7 12
## Eggers (2017),FYTS 0.0299 [0.0225; 0.0389] 0.8 3.5 9
## Eggers (2017),FYTS 0.0279 [0.0209; 0.0363] 0.8 3.5 9
## Eggers (2017),FYTS 0.0438 [0.0351; 0.0539] 1.2 3.6 9
## Eggers (2017),FYTS 0.0897 [0.0768; 0.1040] 2.2 3.6 9
## Eggers (2017),FYTS 0.1090 [0.0947; 0.1247] 2.6 3.6 10
## Eggers (2017),FYTS 0.1279 [0.1110; 0.1463] 2.4 3.6 11
## Eggers (2017),FYTS 0.1348 [0.1166; 0.1546] 2.3 3.6 12
## CDC, NYTS 0.0325 [0.0266; 0.0392] 1.6 3.6 9
## CDC, NYTS 0.0397 [0.0333; 0.0470] 1.9 3.6 9
## CDC, NYTS 0.0567 [0.0489; 0.0653] 2.6 3.6 9
## CDC, NYTS 0.0762 [0.0666; 0.0868] 2.9 3.6 9
## CDC, NYTS 0.1250 [0.1129; 0.1378] 4.7 3.7 10
## CDC, NYTS 0.1402 [0.1273; 0.1540] 4.9 3.7 11
## CDC, NYTS 0.1481 [0.1349; 0.1622] 5.2 3.7 12
## NIH, PATH 0.0073 [0.0020; 0.0187] 0.1 2.4 9
## NIH, PATH 0.0113 [0.0067; 0.0178] 0.3 3.3 9
## NIH, PATH 0.0401 [0.0316; 0.0501] 1.1 3.6 9
## NIH, PATH 0.0755 [0.0640; 0.0883] 2.0 3.6 9
## NIH, PATH 0.1163 [0.1022; 0.1317] 3.0 3.6 10
## NIH, PATH 0.1690 [0.1519; 0.1872] 3.8 3.6 11
## NIH, PATH 0.1974 [0.1787; 0.2172] 4.1 3.6 12
##
## Number of studies combined: k = 28
##
## proportion 95%-CI
## Fixed effect model 0.0812 [0.0794; 0.0831]
## Random effects model 0.0618 [0.0483; 0.0788]
##
## Quantifying heterogeneity:
## tau^2 = 0.4818 [0.3702; 1.2063]; tau = 0.6941 [0.6084; 1.0983]
## I^2 = 99.1% [99.0%; 99.2%]; H = 10.53 [9.86; 11.25]
##
## Test of heterogeneity:
## Q d.f. p-value
## 2995.95 27 0
##
## Results for subgroups (fixed effect model):
## k proportion 95%-CI Q I^2
## byvar = 10 4 0.0957 [0.0912; 0.1005] 78.04 96.2%
## byvar = 11 4 0.1215 [0.1160; 0.1272] 84.47 96.4%
## byvar = 12 4 0.1384 [0.1322; 0.1448] 81.81 96.3%
## byvar = 9 16 0.0424 [0.0409; 0.0441] 776.62 98.1%
##
## Test for subgroup differences (fixed effect model):
## Q d.f. p-value
## Between groups 1975.00 3 0
## Within groups 1020.94 24 < 0.0001
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau
## byvar = 10 4 0.1050 [0.0805; 0.1359] 0.0852 0.2919
## byvar = 11 4 0.1316 [0.1015; 0.1689] 0.0860 0.2932
## byvar = 12 4 0.1459 [0.1134; 0.1858] 0.0837 0.2892
## byvar = 9 16 0.0347 [0.0260; 0.0463] 0.3524 0.5936
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 66.53 3 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_LT, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b## proportion 95%-CI %W(fixed) %W(random)
## Florida Health,FYTS 0.0104 [0.0086; 0.0124] 1.8 5.5
## Florida Health,FYTS 0.0185 [0.0161; 0.0211] 3.1 5.6
## Florida Health,FYTS 0.0361 [0.0329; 0.0396] 6.2 5.6
## Florida Health,FYTS 0.0573 [0.0529; 0.0621] 8.0 5.6
## Florida Health,FYTS 0.0769 [0.0716; 0.0825] 9.7 5.6
## Florida Health,FYTS 0.0984 [0.0918; 0.1053] 9.9 5.6
## Florida Health,FYTS 0.1138 [0.1059; 0.1220] 9.0 5.6
## Eggers (2017),FYTS 0.0299 [0.0225; 0.0389] 0.8 5.3
## Eggers (2017),FYTS 0.0279 [0.0209; 0.0363] 0.8 5.3
## Eggers (2017),FYTS 0.0438 [0.0351; 0.0539] 1.2 5.4
## Eggers (2017),FYTS 0.0897 [0.0768; 0.1040] 2.1 5.5
## Eggers (2017),FYTS 0.1090 [0.0947; 0.1247] 2.5 5.6
## Eggers (2017),FYTS 0.1279 [0.1110; 0.1463] 2.3 5.6
## Eggers (2017),FYTS 0.1348 [0.1166; 0.1546] 2.2 5.5
## Trivers (2018),NYTS 0.0890 [0.0851; 0.0930] 24.7 5.7
## Bentivegna (2020),PATH 0.0824 [0.0763; 0.0888] 8.4 5.6
## Morean (2015), 0.0541 [0.0471; 0.0617] 2.9 5.6
## Peters (2018),HHS 0.1048 [0.0944; 0.1160] 4.4 5.6
##
## Number of studies combined: k = 18
##
## proportion 95%-CI
## Fixed effect model 0.0751 [0.0735; 0.0768]
## Random effects model 0.0607 [0.0475; 0.0771]
##
## Quantifying heterogeneity:
## tau^2 = 0.3054 [0.2341; 1.0621]; tau = 0.5526 [0.4839; 1.0306]
## I^2 = 99.1% [98.9%; 99.2%]; H = 10.46 [9.62; 11.37]
##
## Test of heterogeneity:
## Q d.f. p-value
## 1859.31 17 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studiesforest(b,digits = 3,transf= transf.ilogit.int)
Prevalence by year 2017-18
Meta-analysis
## proportion 95%-CI %W(fixed) %W(random) byvar
## Florida Health,FYTS 0.0086 [0.0049; 0.0139] 0.1 2.2 9
## Florida Health,FYTS 0.0232 [0.0170; 0.0309] 0.4 2.4 9
## Florida Health,FYTS 0.0365 [0.0284; 0.0460] 0.6 2.4 9
## Florida Health,FYTS 0.0567 [0.0460; 0.0689] 0.8 2.4 9
## Florida Health,FYTS 0.0589 [0.0477; 0.0718] 0.8 2.4 10
## Florida Health,FYTS 0.1066 [0.0912; 0.1236] 1.2 2.5 11
## Florida Health,FYTS 0.1295 [0.1103; 0.1507] 1.1 2.5 12
## Florida Health,FYTS 0.0105 [0.0086; 0.0127] 0.9 2.4 9
## Florida Health,FYTS 0.0212 [0.0184; 0.0243] 1.8 2.5 9
## Florida Health,FYTS 0.0415 [0.0377; 0.0456] 3.6 2.5 9
## Florida Health,FYTS 0.0757 [0.0702; 0.0816] 5.3 2.5 9
## Florida Health,FYTS 0.0962 [0.0896; 0.1030] 5.8 2.5 10
## Florida Health,FYTS 0.1250 [0.1172; 0.1332] 6.6 2.5 11
## Florida Health,FYTS 0.1459 [0.1366; 0.1557] 6.0 2.5 12
## CDC, NYTS 0.0301 [0.0236; 0.0379] 0.6 2.4 9
## CDC, NYTS 0.0350 [0.0280; 0.0431] 0.7 2.4 9
## CDC, NYTS 0.0692 [0.0592; 0.0803] 1.3 2.5 9
## CDC, NYTS 0.1067 [0.0948; 0.1196] 2.1 2.5 9
## CDC, NYTS 0.1437 [0.1302; 0.1580] 2.8 2.5 10
## CDC, NYTS 0.1847 [0.1696; 0.2005] 3.3 2.5 11
## CDC, NYTS 0.2188 [0.2019; 0.2363] 3.5 2.5 12
## CDC, NYTS 0.0289 [0.0228; 0.0361] 0.7 2.4 9
## CDC, NYTS 0.0530 [0.0452; 0.0618] 1.3 2.5 9
## CDC, NYTS 0.0816 [0.0718; 0.0923] 1.9 2.5 9
## CDC, NYTS 0.1455 [0.1326; 0.1592] 3.1 2.5 9
## CDC, NYTS 0.2130 [0.1972; 0.2294] 3.8 2.5 10
## CDC, NYTS 0.2443 [0.2282; 0.2610] 4.5 2.5 11
## CDC, NYTS 0.2771 [0.2595; 0.2953] 4.4 2.5 12
## NIH, PATH 0.0085 [0.0028; 0.0197] 0.0 1.8 9
## NIH, PATH 0.0162 [0.0108; 0.0231] 0.3 2.3 9
## NIH, PATH 0.0369 [0.0296; 0.0454] 0.7 2.4 9
## NIH, PATH 0.0658 [0.0558; 0.0770] 1.2 2.5 9
## NIH, PATH 0.1140 [0.1013; 0.1277] 2.1 2.5 10
## NIH, PATH 0.1445 [0.1301; 0.1599] 2.4 2.5 11
## NIH, PATH 0.1796 [0.1638; 0.1963] 2.9 2.5 12
## Miech (2020),MTF 0.0399 [0.0346; 0.0458] 1.7 2.5 9
## Miech (2020),MTF 0.0980 [0.0894; 0.1071] 3.5 2.5 10
## Miech (2020),MTF 0.1189 [0.1092; 0.1292] 3.8 2.5 12
## Miech (2020),MTF 0.0550 [0.0485; 0.0621] 2.1 2.5 9
## Miech (2020),MTF 0.1420 [0.1321; 0.1523] 5.1 2.5 10
## Miech (2020),MTF 0.1559 [0.1452; 0.1671] 5.1 2.5 12
##
## Number of studies combined: k = 41
##
## proportion 95%-CI
## Fixed effect model 0.1122 [0.1104; 0.1141]
## Random effects model 0.0731 [0.0594; 0.0897]
##
## Quantifying heterogeneity:
## tau^2 = 0.5172 [0.4515; 1.1846]; tau = 0.7192 [0.6720; 1.0884]
## I^2 = 99.3% [99.2%; 99.4%]; H = 11.84 [11.26; 12.44]
##
## Test of heterogeneity:
## Q d.f. p-value
## 5606.20 40 0
##
## Results for subgroups (fixed effect model):
## k proportion 95%-CI Q I^2
## byvar = 10 7 0.1254 [0.1213; 0.1297] 341.42 98.2%
## byvar = 11 5 0.1613 [0.1555; 0.1673] 245.25 98.4%
## byvar = 12 7 0.1725 [0.1674; 0.1776] 347.29 98.3%
## byvar = 9 22 0.0554 [0.0537; 0.0571] 1460.40 98.6%
##
## Test for subgroup differences (fixed effect model):
## Q d.f. p-value
## Between groups 3211.84 3 0
## Within groups 2394.35 37 0
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau
## byvar = 10 7 0.1172 [0.0902; 0.1511] 0.1520 0.3898
## byvar = 11 5 0.1558 [0.1147; 0.2081] 0.1596 0.3995
## byvar = 12 7 0.1699 [0.1344; 0.2125] 0.1361 0.3690
## byvar = 9 22 0.0386 [0.0293; 0.0506] 0.4474 0.6689
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 76.95 3 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_LT, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b## proportion 95%-CI %W(fixed) %W(random)
## Florida Health,FYTS 0.0086 [0.0049; 0.0139] 0.1 2.9
## Florida Health,FYTS 0.0232 [0.0170; 0.0309] 0.4 3.2
## Florida Health,FYTS 0.0365 [0.0284; 0.0460] 0.5 3.3
## Florida Health,FYTS 0.0567 [0.0460; 0.0689] 0.7 3.3
## Florida Health,FYTS 0.0589 [0.0477; 0.0718] 0.7 3.3
## Florida Health,FYTS 0.1066 [0.0912; 0.1236] 1.2 3.4
## Florida Health,FYTS 0.1295 [0.1103; 0.1507] 1.1 3.4
## Florida Health,FYTS 0.0105 [0.0086; 0.0127] 0.9 3.4
## Florida Health,FYTS 0.0212 [0.0184; 0.0243] 1.7 3.4
## Florida Health,FYTS 0.0415 [0.0377; 0.0456] 3.4 3.4
## Florida Health,FYTS 0.0757 [0.0702; 0.0816] 5.0 3.4
## Florida Health,FYTS 0.0962 [0.0896; 0.1030] 5.5 3.4
## Florida Health,FYTS 0.1250 [0.1172; 0.1332] 6.1 3.4
## Florida Health,FYTS 0.1459 [0.1366; 0.1557] 5.6 3.4
## NIH, PATH 0.0085 [0.0028; 0.0197] 0.0 2.1
## NIH, PATH 0.0162 [0.0108; 0.0231] 0.2 3.1
## NIH, PATH 0.0369 [0.0296; 0.0454] 0.7 3.3
## NIH, PATH 0.0658 [0.0558; 0.0770] 1.1 3.4
## NIH, PATH 0.1140 [0.1013; 0.1277] 2.0 3.4
## NIH, PATH 0.1445 [0.1301; 0.1599] 2.3 3.4
## NIH, PATH 0.1796 [0.1638; 0.1963] 2.7 3.4
## Miech (2020),MTF 0.0399 [0.0346; 0.0458] 1.6 3.4
## Miech (2020),MTF 0.0980 [0.0894; 0.1071] 3.3 3.4
## Miech (2020),MTF 0.1189 [0.1092; 0.1292] 3.6 3.4
## Miech (2020),MTF 0.0550 [0.0485; 0.0621] 2.0 3.4
## Miech (2020),MTF 0.1420 [0.1321; 0.1523] 4.8 3.4
## Miech (2020),MTF 0.1559 [0.1452; 0.1671] 4.7 3.4
## Dai (2020),NYTS 0.1110 [0.1064; 0.1157] 14.8 3.5
## Dai (2020),NYTS 0.1470 [0.1422; 0.1520] 21.2 3.5
## Kowitt (2019),NCYTS 0.0959 [0.0853; 0.1074] 2.1 3.4
##
## Number of studies combined: k = 30
##
## proportion 95%-CI
## Fixed effect model 0.1061 [0.1044; 0.1078]
## Random effects model 0.0652 [0.0537; 0.0790]
##
## Quantifying heterogeneity:
## tau^2 = 0.3211 [0.3197; 1.0672]; tau = 0.5667 [0.5654; 1.0330]
## I^2 = 99.2% [99.1%; 99.3%]; H = 11.00 [10.34; 11.70]
##
## Test of heterogeneity:
## Q d.f. p-value
## 3510.47 29 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studiesforest(b,digits = 3,transf= transf.ilogit.int)
Prevalence by year 2019-20
Meta-analysis
## proportion 95%-CI %W(fixed) %W(random) byvar
## Florida Health,FYTS 0.0152 [0.0099; 0.0224] 0.2 3.9 9
## Florida Health,FYTS 0.0343 [0.0261; 0.0443] 0.5 4.1 9
## Florida Health,FYTS 0.0663 [0.0551; 0.0788] 1.0 4.1 9
## Florida Health,FYTS 0.1062 [0.0922; 0.1216] 1.5 4.2 9
## Florida Health,FYTS 0.1349 [0.1176; 0.1538] 1.5 4.2 10
## Florida Health,FYTS 0.1646 [0.1454; 0.1853] 1.7 4.2 11
## Florida Health,FYTS 0.2004 [0.1772; 0.2251] 1.6 4.2 12
## Florida Health,FYTS 0.0363 [0.0311; 0.0421] 1.5 4.2 9
## Florida Health,FYTS 0.0824 [0.0748; 0.0904] 3.4 4.2 9
## Florida Health,FYTS 0.1560 [0.1462; 0.1661] 6.2 4.2 9
## Florida Health,FYTS 0.2289 [0.2167; 0.2415] 7.2 4.2 9
## Florida Health,FYTS 0.3114 [0.2976; 0.3254] 8.5 4.2 10
## Florida Health,FYTS 0.3483 [0.3330; 0.3640] 7.6 4.2 11
## Florida Health,FYTS 0.3268 [0.3106; 0.3433] 6.4 4.2 12
## CDC, NYTS 0.0256 [0.0196; 0.0328] 0.5 4.1 9
## CDC, NYTS 0.0555 [0.0465; 0.0655] 1.1 4.1 9
## CDC, NYTS 0.0931 [0.0816; 0.1056] 1.8 4.2 9
## CDC, NYTS 0.1790 [0.1623; 0.1967] 2.6 4.2 9
## CDC, NYTS 0.2787 [0.2585; 0.2996] 3.4 4.2 10
## CDC, NYTS 0.3200 [0.2984; 0.3421] 3.5 4.2 11
## CDC, NYTS 0.3798 [0.3573; 0.4027] 3.9 4.2 12
## Miech (2020),MTF 0.0900 [0.0841; 0.0962] 6.5 4.2 9
## Miech (2020),MTF 0.2180 [0.2096; 0.2267] 14.1 4.2 10
## Miech (2020),MTF 0.2371 [0.2280; 0.2463] 13.8 4.2 12
##
## Number of studies combined: k = 24
##
## proportion 95%-CI
## Fixed effect model 0.2064 [0.2034; 0.2095]
## Random effects model 0.1339 [0.1048; 0.1696]
##
## Quantifying heterogeneity:
## tau^2 = 0.4767 [0.4129; 1.5129]; tau = 0.6904 [0.6426; 1.2300]
## I^2 = 99.5% [99.5%; 99.6%]; H = 14.54 [13.74; 15.40]
##
## Test of heterogeneity:
## Q d.f. p-value
## 4864.62 23 0
##
## Results for subgroups (fixed effect model):
## k proportion 95%-CI Q I^2
## byvar = 10 4 0.2461 [0.2395; 0.2528] 238.57 98.7%
## byvar = 11 3 0.3111 [0.3000; 0.3223] 154.88 98.7%
## byvar = 12 4 0.2751 [0.2678; 0.2825] 231.19 98.7%
## byvar = 9 13 0.1168 [0.1135; 0.1201] 1536.45 99.2%
##
## Test for subgroup differences (fixed effect model):
## Q d.f. p-value
## Between groups 2703.53 3 0
## Within groups 2161.09 20 0
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau
## byvar = 10 4 0.2289 [0.1730; 0.2964] 0.1250 0.3536
## byvar = 11 3 0.2694 [0.1825; 0.3785] 0.1936 0.4400
## byvar = 12 4 0.2811 [0.2149; 0.3583] 0.1297 0.3602
## byvar = 9 13 0.0718 [0.0503; 0.1013] 0.4733 0.6880
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 45.95 3 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_LT, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b## proportion 95%-CI %W(fixed) %W(random)
## Florida Health,FYTS 0.0152 [0.0099; 0.0224] 0.2 3.7
## Florida Health,FYTS 0.0343 [0.0261; 0.0443] 0.4 3.9
## Florida Health,FYTS 0.0663 [0.0551; 0.0788] 0.8 4.0
## Florida Health,FYTS 0.1062 [0.0922; 0.1216] 1.3 4.0
## Florida Health,FYTS 0.1349 [0.1176; 0.1538] 1.3 4.0
## Florida Health,FYTS 0.1646 [0.1454; 0.1853] 1.4 4.0
## Florida Health,FYTS 0.2004 [0.1772; 0.2251] 1.4 4.0
## Florida Health,FYTS 0.0363 [0.0311; 0.0421] 1.2 4.0
## Florida Health,FYTS 0.0824 [0.0748; 0.0904] 2.8 4.0
## Florida Health,FYTS 0.1560 [0.1462; 0.1661] 5.2 4.0
## Florida Health,FYTS 0.2289 [0.2167; 0.2415] 6.0 4.0
## Florida Health,FYTS 0.3114 [0.2976; 0.3254] 7.1 4.0
## Florida Health,FYTS 0.3483 [0.3330; 0.3640] 6.4 4.0
## Florida Health,FYTS 0.3268 [0.3106; 0.3433] 5.4 4.0
## CDC, NYTS 0.0256 [0.0196; 0.0328] 0.4 3.9
## CDC, NYTS 0.0555 [0.0465; 0.0655] 0.9 4.0
## CDC, NYTS 0.0931 [0.0816; 0.1056] 1.5 4.0
## CDC, NYTS 0.1790 [0.1623; 0.1967] 2.2 4.0
## CDC, NYTS 0.2787 [0.2585; 0.2996] 2.9 4.0
## CDC, NYTS 0.3200 [0.2984; 0.3421] 3.0 4.0
## CDC, NYTS 0.3798 [0.3573; 0.4027] 3.2 4.0
## CDC, NYTS 0.1756 [0.1695; 0.1819] 16.0 4.1
## Miech (2020),MTF 0.0900 [0.0841; 0.0962] 5.5 4.0
## Miech (2020),MTF 0.2180 [0.2096; 0.2267] 11.8 4.1
## Miech (2020),MTF 0.2371 [0.2280; 0.2463] 11.6 4.1
##
## Number of studies combined: k = 25
##
## proportion 95%-CI
## Fixed effect model 0.2013 [0.1985; 0.2040]
## Random effects model 0.1357 [0.1087; 0.1680]
##
## Quantifying heterogeneity:
## tau^2 = 0.4076 [0.3767; 1.3608]; tau = 0.6384 [0.6137; 1.1665]
## I^2 = 99.5% [99.5%; 99.6%]; H = 14.34 [13.55; 15.17]
##
## Test of heterogeneity:
## Q d.f. p-value
## 4934.75 24 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studiesforest(b,digits = 3,transf= transf.ilogit.int)
(REGROUP) 12-m National + State surveys
Prevalence by year 2017-18
Meta-analysis
## proportion 95%-CI %W(fixed) %W(random) byvar
## Miech (2020),MTF 0.0299 [0.0253; 0.0351] 7.1 16.5 9
## Miech (2020),MTF 0.0810 [0.0731; 0.0894] 16.4 16.7 10
## Miech (2020),MTF 0.0950 [0.0862; 0.1045] 17.5 16.7 12
## Miech (2020),MTF 0.0441 [0.0382; 0.0505] 9.4 16.6 9
## Miech (2020),MTF 0.1240 [0.1146; 0.1337] 25.4 16.8 10
## Miech (2020),MTF 0.1310 [0.1210; 0.1415] 24.2 16.8 12
##
## Number of studies combined: k = 6
##
## proportion 95%-CI
## Fixed effect model 0.0925 [0.0889; 0.0962]
## Random effects model 0.0751 [0.0503; 0.1106]
##
## Quantifying heterogeneity:
## tau^2 = 0.2803 [0.1298; 2.1247]; tau = 0.5295 [0.3603; 1.4576]
## I^2 = 98.9% [98.5%; 99.2%]; H = 9.56 [8.12; 11.24]
##
## Test of heterogeneity:
## Q d.f. p-value
## 456.77 5 < 0.0001
##
## Results for subgroups (fixed effect model):
## k proportion 95%-CI Q I^2
## byvar = 10 2 0.1051 [0.0989; 0.1116] 44.82 97.8%
## byvar = 12 2 0.1147 [0.1079; 0.1217] 26.50 96.2%
## byvar = 9 2 0.0373 [0.0337; 0.0414] 13.02 92.3%
##
## Test for subgroup differences (fixed effect model):
## Q d.f. p-value
## Between groups 372.42 2 < 0.0001
## Within groups 84.35 3 < 0.0001
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau
## byvar = 10 2 0.1005 [0.0656; 0.1510] 0.1098 0.3313
## byvar = 12 2 0.1119 [0.0812; 0.1521] 0.0628 0.2505
## byvar = 9 2 0.0364 [0.0249; 0.0530] 0.0744 0.2727
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 22.25 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
## proportion 95%-CI %W(fixed) %W(random)
## CAMH, OSDUHS 0.0689 [0.0605; 0.0782] 4.5 12.4
## Miech (2020),MTF 0.0299 [0.0253; 0.0351] 3.0 12.3
## Miech (2020),MTF 0.0810 [0.0731; 0.0894] 7.0 12.5
## Miech (2020),MTF 0.0950 [0.0862; 0.1045] 7.5 12.5
## Miech (2020),MTF 0.0441 [0.0382; 0.0505] 4.0 12.4
## Miech (2020),MTF 0.1240 [0.1146; 0.1337] 10.9 12.6
## Miech (2020),MTF 0.1310 [0.1210; 0.1415] 10.4 12.6
## Doggett (2020),COMPASS 0.0572 [0.0551; 0.0593] 52.7 12.7
##
## Number of studies combined: k = 8
##
## proportion 95%-CI
## Fixed effect model 0.0710 [0.0691; 0.0729]
## Random effects model 0.0718 [0.0523; 0.0979]
##
## Quantifying heterogeneity:
## tau^2 = 0.2338 [0.1018; 1.1179]; tau = 0.4835 [0.3191; 1.0573]
## I^2 = 99.1% [98.8%; 99.3%]; H = 10.38 [9.12; 11.83]
##
## Test of heterogeneity:
## Q d.f. p-value
## 754.51 7 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
Prevalence by year 2019-20
Meta-analysis
## proportion 95%-CI %W(fixed) %W(random) byvar
## Miech (2020),MTF 0.0700 [0.0647; 0.0756] 16.9 33.3 9
## Miech (2020),MTF 0.1940 [0.1858; 0.2023] 42.2 33.4 10
## Miech (2020),MTF 0.2080 [0.1993; 0.2169] 40.9 33.4 12
##
## Number of studies combined: k = 3
##
## proportion 95%-CI
## Fixed effect model 0.1700 [0.1653; 0.1749]
## Random effects model 0.1441 [0.0809; 0.2436]
##
## Quantifying heterogeneity:
## tau^2 = 0.3277 [0.1089; 16.3914]; tau = 0.5725 [0.3301; 4.0486]
## I^2 = 99.7% [99.6%; 99.8%]; H = 18.56 [15.79; 21.82]
##
## Test of heterogeneity:
## Q d.f. p-value
## 689.06 2 < 0.0001
##
## Results for subgroups (fixed effect model):
## k proportion 95%-CI Q I^2
## byvar = 10 1 0.1940 [0.1859; 0.2022] 0.00 --
## byvar = 12 1 0.2080 [0.1994; 0.2169] 0.00 --
## byvar = 9 1 0.0700 [0.0648; 0.0755] 0.00 --
##
## Test for subgroup differences (fixed effect model):
## Q d.f. p-value
## Between groups 689.06 2 < 0.0001
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau
## byvar = 10 1 0.1940 [0.1859; 0.2022] -- --
## byvar = 12 1 0.2080 [0.1994; 0.2169] -- --
## byvar = 9 1 0.0700 [0.0648; 0.0755] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 689.06 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_12m, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b## proportion 95%-CI %W(fixed) %W(random)
## CAMH, OSDUHS 0.0999 [0.0914; 0.1090] 11.0 24.9
## Miech (2020),MTF 0.0700 [0.0647; 0.0756] 15.1 25.0
## Miech (2020),MTF 0.1940 [0.1858; 0.2023] 37.5 25.1
## Miech (2020),MTF 0.2080 [0.1993; 0.2169] 36.4 25.1
##
## Number of studies combined: k = 4
##
## proportion 95%-CI
## Fixed effect model 0.1608 [0.1565; 0.1651]
## Random effects model 0.1318 [0.0803; 0.2087]
##
## Quantifying heterogeneity:
## tau^2 = 0.3168 [0.1108; 5.0724]; tau = 0.5628 [0.3329; 2.2522]
## I^2 = 99.6% [99.5%; 99.7%]; H = 16.60 [14.39; 19.16]
##
## Test of heterogeneity:
## Q d.f. p-value
## 827.08 3 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studiesforest(b,digits = 3,transf= transf.ilogit.int)
(REGROUP) 30d National + State surveys
Prevalence by year 2013-16
Meta-analysis
## proportion 95%-CI %W(fixed) %W(random) byvar
## Johnson (2016),HKCS 0.0050 [0.0029; 0.0080] 11.3 23.3 9
## Johnson (2016),HKCS 0.0098 [0.0067; 0.0138] 21.2 25.0 10
## Johnson (2016),HKCS 0.0129 [0.0093; 0.0174] 27.7 25.6 11
## Johnson (2016),HKCS 0.0234 [0.0180; 0.0300] 39.8 26.1 12
##
## Number of studies combined: k = 4
##
## proportion 95%-CI
## Fixed effect model 0.0139 [0.0119; 0.0163]
## Random effects model 0.0113 [0.0062; 0.0204]
##
## Quantifying heterogeneity:
## tau^2 = 0.3401 [0.0963; 5.5794]; tau = 0.5832 [0.3104; 2.3621]
## I^2 = 92.3% [83.5%; 96.4%]; H = 3.60 [2.46; 5.28]
##
## Test of heterogeneity:
## Q d.f. p-value
## 38.98 3 < 0.0001
##
## Results for subgroups (fixed effect model):
## k proportion 95%-CI Q I^2
## byvar = 10 1 0.0098 [0.0069; 0.0138] 0.00 --
## byvar = 11 1 0.0129 [0.0095; 0.0174] 0.00 --
## byvar = 12 1 0.0234 [0.0183; 0.0300] 0.00 --
## byvar = 9 1 0.0050 [0.0031; 0.0080] 0.00 --
##
## Test for subgroup differences (fixed effect model):
## Q d.f. p-value
## Between groups 38.98 3 < 0.0001
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau
## byvar = 10 1 0.0098 [0.0069; 0.0138] -- --
## byvar = 11 1 0.0129 [0.0095; 0.0174] -- --
## byvar = 12 1 0.0234 [0.0183; 0.0300] -- --
## byvar = 9 1 0.0050 [0.0031; 0.0080] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 38.98 3 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_30d, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b## proportion 95%-CI %W(fixed) %W(random)
## CDPHE,HKCS 0.0440 [0.0363; 0.0527] 25.3 14.7
## Barrington-Trimis (2020), 0.0060 [0.0034; 0.0097] 3.8 13.6
## Johnson (2016),HKCS 0.0050 [0.0029; 0.0080] 4.0 13.7
## Johnson (2016),HKCS 0.0098 [0.0067; 0.0138] 7.5 14.2
## Johnson (2016),HKCS 0.0129 [0.0093; 0.0174] 9.9 14.4
## Johnson (2016),HKCS 0.0234 [0.0180; 0.0300] 14.2 14.5
## Peters (2018),HHS 0.0491 [0.0418; 0.0572] 35.3 14.8
##
## Number of studies combined: k = 7
##
## proportion 95%-CI
## Fixed effect model 0.0283 [0.0258; 0.0310]
## Random effects model 0.0157 [0.0084; 0.0290]
##
## Quantifying heterogeneity:
## tau^2 = 0.6857 [0.3145; 4.3052]; tau = 0.8281 [0.5608; 2.0749]
## I^2 = 97.4% [96.1%; 98.2%]; H = 6.17 [5.07; 7.52]
##
## Test of heterogeneity:
## Q d.f. p-value
## 228.55 6 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studiesforest(b,digits = 3,transf= transf.ilogit.int)
Prevalence by year 2017-18
Meta-analysis
## proportion 95%-CI %W(fixed) %W(random) byvar
## Miech (2020),MTF 0.0160 [0.0127; 0.0200] 6.6 16.3 9
## Miech (2020),MTF 0.0431 [0.0373; 0.0495] 15.6 16.7 10
## Miech (2020),MTF 0.0501 [0.0436; 0.0572] 16.6 16.7 12
## Miech (2020),MTF 0.0260 [0.0215; 0.0311] 9.7 16.5 9
## Miech (2020),MTF 0.0701 [0.0629; 0.0778] 26.1 16.9 10
## Miech (2020),MTF 0.0751 [0.0674; 0.0835] 25.3 16.9 12
##
## Number of studies combined: k = 6
##
## proportion 95%-CI
## Fixed effect model 0.0517 [0.0490; 0.0546]
## Random effects model 0.0415 [0.0277; 0.0618]
##
## Quantifying heterogeneity:
## tau^2 = 0.2683 [0.1219; 2.0404]; tau = 0.5180 [0.3492; 1.4284]
## I^2 = 98.0% [97.1%; 98.7%]; H = 7.15 [5.87; 8.70]
##
## Test of heterogeneity:
## Q d.f. p-value
## 255.49 5 < 0.0001
##
## Results for subgroups (fixed effect model):
## k proportion 95%-CI Q I^2
## byvar = 10 2 0.0585 [0.0538; 0.0636] 30.25 96.7%
## byvar = 12 2 0.0640 [0.0589; 0.0696] 21.82 95.4%
## byvar = 9 2 0.0214 [0.0186; 0.0246] 11.14 91.0%
##
## Test for subgroup differences (fixed effect model):
## Q d.f. p-value
## Between groups 192.28 2 < 0.0001
## Within groups 63.21 3 < 0.0001
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau
## byvar = 10 2 0.0551 [0.0340; 0.0882] 0.1285 0.3585
## byvar = 12 2 0.0615 [0.0412; 0.0910] 0.0890 0.2983
## byvar = 9 2 0.0205 [0.0127; 0.0328] 0.1109 0.3330
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 13.67 2 0.0011
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_30d, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b## proportion 95%-CI %W(fixed) %W(random)
## CDPHE,HKCS 0.0400 [0.0342; 0.0465] 9.9 12.5
## Miech (2020),MTF 0.0160 [0.0127; 0.0200] 4.8 12.2
## Miech (2020),MTF 0.0431 [0.0373; 0.0495] 11.5 12.5
## Miech (2020),MTF 0.0501 [0.0436; 0.0572] 12.2 12.5
## Miech (2020),MTF 0.0260 [0.0215; 0.0311] 7.1 12.4
## Miech (2020),MTF 0.0701 [0.0629; 0.0778] 19.2 12.6
## Miech (2020),MTF 0.0751 [0.0674; 0.0835] 18.6 12.6
## Nguyen (2019),HHS 0.0967 [0.0864; 0.1079] 16.6 12.6
##
## Number of studies combined: k = 8
##
## proportion 95%-CI
## Fixed effect model 0.0561 [0.0535; 0.0588]
## Random effects model 0.0461 [0.0324; 0.0650]
##
## Quantifying heterogeneity:
## tau^2 = 0.2714 [0.1367; 1.3832]; tau = 0.5209 [0.3697; 1.1761]
## I^2 = 98.1% [97.4%; 98.7%]; H = 7.32 [6.21; 8.62]
##
## Test of heterogeneity:
## Q d.f. p-value
## 374.94 7 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studiesforest(b,digits = 3,transf= transf.ilogit.int)
Prevalence by year 2019-20
Meta-analysis
## proportion 95%-CI %W(fixed) %W(random) byvar
## Miech (2020),MTF 0.0390 [0.0350; 0.0432] 14.0 33.2 9
## Miech (2020),MTF 0.1260 [0.1192; 0.1330] 42.8 33.4 10
## Miech (2020),MTF 0.1400 [0.1326; 0.1476] 43.1 33.4 12
##
## Number of studies combined: k = 3
##
## proportion 95%-CI
## Fixed effect model 0.1128 [0.1088; 0.1169]
## Random effects model 0.0897 [0.0478; 0.1620]
##
## Quantifying heterogeneity:
## tau^2 = 0.3530 [0.1253; 19.1725]; tau = 0.5942 [0.3540; 4.3786]
## I^2 = 99.6% [99.4%; 99.7%]; H = 15.85 [13.23; 18.99]
##
## Test of heterogeneity:
## Q d.f. p-value
## 502.42 2 < 0.0001
##
## Results for subgroups (fixed effect model):
## k proportion 95%-CI Q I^2
## byvar = 10 1 0.1260 [0.1193; 0.1330] 0.00 --
## byvar = 12 1 0.1400 [0.1327; 0.1476] 0.00 --
## byvar = 9 1 0.0390 [0.0351; 0.0432] 0.00 --
##
## Test for subgroup differences (fixed effect model):
## Q d.f. p-value
## Between groups 502.42 2 < 0.0001
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau
## byvar = 10 1 0.1260 [0.1193; 0.1330] -- --
## byvar = 12 1 0.1400 [0.1327; 0.1476] -- --
## byvar = 9 1 0.0390 [0.0351; 0.0432] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 502.42 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_30d, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b## proportion 95%-CI %W(fixed) %W(random)
## CDPHE,HKCS 0.0680 [0.0612; 0.0754] 12.0 24.9
## Miech (2020),MTF 0.0390 [0.0350; 0.0432] 12.4 24.9
## Miech (2020),MTF 0.1260 [0.1192; 0.1330] 37.7 25.1
## Miech (2020),MTF 0.1400 [0.1326; 0.1476] 38.0 25.1
##
## Number of studies combined: k = 4
##
## proportion 95%-CI
## Fixed effect model 0.1063 [0.1027; 0.1100]
## Random effects model 0.0838 [0.0496; 0.1380]
##
## Quantifying heterogeneity:
## tau^2 = 0.3244 [0.1194; 5.5102]; tau = 0.5696 [0.3455; 2.3474]
## I^2 = 99.5% [99.3%; 99.6%]; H = 14.00 [11.91; 16.45]
##
## Test of heterogeneity:
## Q d.f. p-value
## 587.82 3 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studiesforest(b,digits = 3,transf= transf.ilogit.int)
LT National + State surveys (U.S)
Prevalence by year 2013-16
Meta-analysis
## proportion 95%-CI %W(fixed) %W(random) byvar
## Florida Health,FYTS 0.0104 [0.0086; 0.0124] 1.8 3.6 9
## Florida Health,FYTS 0.0185 [0.0161; 0.0211] 3.3 3.6 9
## Florida Health,FYTS 0.0361 [0.0329; 0.0396] 6.5 3.7 9
## Florida Health,FYTS 0.0573 [0.0529; 0.0621] 8.3 3.7 9
## Florida Health,FYTS 0.0769 [0.0716; 0.0825] 10.1 3.7 10
## Florida Health,FYTS 0.0984 [0.0918; 0.1053] 10.3 3.7 11
## Florida Health,FYTS 0.1138 [0.1059; 0.1220] 9.3 3.7 12
## Eggers (2017),FYTS 0.0299 [0.0225; 0.0389] 0.8 3.5 9
## Eggers (2017),FYTS 0.0279 [0.0209; 0.0363] 0.8 3.5 9
## Eggers (2017),FYTS 0.0438 [0.0351; 0.0539] 1.2 3.6 9
## Eggers (2017),FYTS 0.0897 [0.0768; 0.1040] 2.2 3.6 9
## Eggers (2017),FYTS 0.1090 [0.0947; 0.1247] 2.6 3.6 10
## Eggers (2017),FYTS 0.1279 [0.1110; 0.1463] 2.4 3.6 11
## Eggers (2017),FYTS 0.1348 [0.1166; 0.1546] 2.3 3.6 12
## CDC, NYTS 0.0325 [0.0266; 0.0392] 1.6 3.6 9
## CDC, NYTS 0.0397 [0.0333; 0.0470] 1.9 3.6 9
## CDC, NYTS 0.0567 [0.0489; 0.0653] 2.6 3.6 9
## CDC, NYTS 0.0762 [0.0666; 0.0868] 2.9 3.6 9
## CDC, NYTS 0.1250 [0.1129; 0.1378] 4.7 3.7 10
## CDC, NYTS 0.1402 [0.1273; 0.1540] 4.9 3.7 11
## CDC, NYTS 0.1481 [0.1349; 0.1622] 5.2 3.7 12
## NIH, PATH 0.0073 [0.0020; 0.0187] 0.1 2.4 9
## NIH, PATH 0.0113 [0.0067; 0.0178] 0.3 3.3 9
## NIH, PATH 0.0401 [0.0316; 0.0501] 1.1 3.6 9
## NIH, PATH 0.0755 [0.0640; 0.0883] 2.0 3.6 9
## NIH, PATH 0.1163 [0.1022; 0.1317] 3.0 3.6 10
## NIH, PATH 0.1690 [0.1519; 0.1872] 3.8 3.6 11
## NIH, PATH 0.1974 [0.1787; 0.2172] 4.1 3.6 12
##
## Number of studies combined: k = 28
##
## proportion 95%-CI
## Fixed effect model 0.0812 [0.0794; 0.0831]
## Random effects model 0.0618 [0.0483; 0.0788]
##
## Quantifying heterogeneity:
## tau^2 = 0.4818 [0.3702; 1.2063]; tau = 0.6941 [0.6084; 1.0983]
## I^2 = 99.1% [99.0%; 99.2%]; H = 10.53 [9.86; 11.25]
##
## Test of heterogeneity:
## Q d.f. p-value
## 2995.95 27 0
##
## Results for subgroups (fixed effect model):
## k proportion 95%-CI Q I^2
## byvar = 10 4 0.0957 [0.0912; 0.1005] 78.04 96.2%
## byvar = 11 4 0.1215 [0.1160; 0.1272] 84.47 96.4%
## byvar = 12 4 0.1384 [0.1322; 0.1448] 81.81 96.3%
## byvar = 9 16 0.0424 [0.0409; 0.0441] 776.62 98.1%
##
## Test for subgroup differences (fixed effect model):
## Q d.f. p-value
## Between groups 1975.00 3 0
## Within groups 1020.94 24 < 0.0001
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau
## byvar = 10 4 0.1050 [0.0805; 0.1359] 0.0852 0.2919
## byvar = 11 4 0.1316 [0.1015; 0.1689] 0.0860 0.2932
## byvar = 12 4 0.1459 [0.1134; 0.1858] 0.0837 0.2892
## byvar = 9 16 0.0347 [0.0260; 0.0463] 0.3524 0.5936
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 66.53 3 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_LT, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b## proportion 95%-CI %W(fixed) %W(random)
## Florida Health,FYTS 0.0104 [0.0086; 0.0124] 1.8 5.5
## Florida Health,FYTS 0.0185 [0.0161; 0.0211] 3.1 5.6
## Florida Health,FYTS 0.0361 [0.0329; 0.0396] 6.2 5.6
## Florida Health,FYTS 0.0573 [0.0529; 0.0621] 8.0 5.6
## Florida Health,FYTS 0.0769 [0.0716; 0.0825] 9.7 5.6
## Florida Health,FYTS 0.0984 [0.0918; 0.1053] 9.9 5.6
## Florida Health,FYTS 0.1138 [0.1059; 0.1220] 9.0 5.6
## Eggers (2017),FYTS 0.0299 [0.0225; 0.0389] 0.8 5.3
## Eggers (2017),FYTS 0.0279 [0.0209; 0.0363] 0.8 5.3
## Eggers (2017),FYTS 0.0438 [0.0351; 0.0539] 1.2 5.4
## Eggers (2017),FYTS 0.0897 [0.0768; 0.1040] 2.1 5.5
## Eggers (2017),FYTS 0.1090 [0.0947; 0.1247] 2.5 5.6
## Eggers (2017),FYTS 0.1279 [0.1110; 0.1463] 2.3 5.6
## Eggers (2017),FYTS 0.1348 [0.1166; 0.1546] 2.2 5.5
## Trivers (2018),NYTS 0.0890 [0.0851; 0.0930] 24.7 5.7
## Bentivegna (2020),PATH 0.0824 [0.0763; 0.0888] 8.4 5.6
## Morean (2015), 0.0541 [0.0471; 0.0617] 2.9 5.6
## Peters (2018),HHS 0.1048 [0.0944; 0.1160] 4.4 5.6
##
## Number of studies combined: k = 18
##
## proportion 95%-CI
## Fixed effect model 0.0751 [0.0735; 0.0768]
## Random effects model 0.0607 [0.0475; 0.0771]
##
## Quantifying heterogeneity:
## tau^2 = 0.3054 [0.2341; 1.0621]; tau = 0.5526 [0.4839; 1.0306]
## I^2 = 99.1% [98.9%; 99.2%]; H = 10.46 [9.62; 11.37]
##
## Test of heterogeneity:
## Q d.f. p-value
## 1859.31 17 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studiesforest(b,digits = 3,transf= transf.ilogit.int)
Prevalence by year 2017-18
Meta-analysis
## proportion 95%-CI %W(fixed) %W(random) byvar
## Florida Health,FYTS 0.0086 [0.0049; 0.0139] 0.1 2.2 9
## Florida Health,FYTS 0.0232 [0.0170; 0.0309] 0.4 2.4 9
## Florida Health,FYTS 0.0365 [0.0284; 0.0460] 0.6 2.4 9
## Florida Health,FYTS 0.0567 [0.0460; 0.0689] 0.8 2.4 9
## Florida Health,FYTS 0.0589 [0.0477; 0.0718] 0.8 2.4 10
## Florida Health,FYTS 0.1066 [0.0912; 0.1236] 1.2 2.5 11
## Florida Health,FYTS 0.1295 [0.1103; 0.1507] 1.1 2.5 12
## Florida Health,FYTS 0.0105 [0.0086; 0.0127] 0.9 2.4 9
## Florida Health,FYTS 0.0212 [0.0184; 0.0243] 1.8 2.5 9
## Florida Health,FYTS 0.0415 [0.0377; 0.0456] 3.6 2.5 9
## Florida Health,FYTS 0.0757 [0.0702; 0.0816] 5.3 2.5 9
## Florida Health,FYTS 0.0962 [0.0896; 0.1030] 5.8 2.5 10
## Florida Health,FYTS 0.1250 [0.1172; 0.1332] 6.6 2.5 11
## Florida Health,FYTS 0.1459 [0.1366; 0.1557] 6.0 2.5 12
## CDC, NYTS 0.0301 [0.0236; 0.0379] 0.6 2.4 9
## CDC, NYTS 0.0350 [0.0280; 0.0431] 0.7 2.4 9
## CDC, NYTS 0.0692 [0.0592; 0.0803] 1.3 2.5 9
## CDC, NYTS 0.1067 [0.0948; 0.1196] 2.1 2.5 9
## CDC, NYTS 0.1437 [0.1302; 0.1580] 2.8 2.5 10
## CDC, NYTS 0.1847 [0.1696; 0.2005] 3.3 2.5 11
## CDC, NYTS 0.2188 [0.2019; 0.2363] 3.5 2.5 12
## CDC, NYTS 0.0289 [0.0228; 0.0361] 0.7 2.4 9
## CDC, NYTS 0.0530 [0.0452; 0.0618] 1.3 2.5 9
## CDC, NYTS 0.0816 [0.0718; 0.0923] 1.9 2.5 9
## CDC, NYTS 0.1455 [0.1326; 0.1592] 3.1 2.5 9
## CDC, NYTS 0.2130 [0.1972; 0.2294] 3.8 2.5 10
## CDC, NYTS 0.2443 [0.2282; 0.2610] 4.5 2.5 11
## CDC, NYTS 0.2771 [0.2595; 0.2953] 4.4 2.5 12
## NIH, PATH 0.0085 [0.0028; 0.0197] 0.0 1.8 9
## NIH, PATH 0.0162 [0.0108; 0.0231] 0.3 2.3 9
## NIH, PATH 0.0369 [0.0296; 0.0454] 0.7 2.4 9
## NIH, PATH 0.0658 [0.0558; 0.0770] 1.2 2.5 9
## NIH, PATH 0.1140 [0.1013; 0.1277] 2.1 2.5 10
## NIH, PATH 0.1445 [0.1301; 0.1599] 2.4 2.5 11
## NIH, PATH 0.1796 [0.1638; 0.1963] 2.9 2.5 12
## Miech (2020),MTF 0.0399 [0.0346; 0.0458] 1.7 2.5 9
## Miech (2020),MTF 0.0980 [0.0894; 0.1071] 3.5 2.5 10
## Miech (2020),MTF 0.1189 [0.1092; 0.1292] 3.8 2.5 12
## Miech (2020),MTF 0.0550 [0.0485; 0.0621] 2.1 2.5 9
## Miech (2020),MTF 0.1420 [0.1321; 0.1523] 5.1 2.5 10
## Miech (2020),MTF 0.1559 [0.1452; 0.1671] 5.1 2.5 12
##
## Number of studies combined: k = 41
##
## proportion 95%-CI
## Fixed effect model 0.1122 [0.1104; 0.1141]
## Random effects model 0.0731 [0.0594; 0.0897]
##
## Quantifying heterogeneity:
## tau^2 = 0.5172 [0.4515; 1.1846]; tau = 0.7192 [0.6720; 1.0884]
## I^2 = 99.3% [99.2%; 99.4%]; H = 11.84 [11.26; 12.44]
##
## Test of heterogeneity:
## Q d.f. p-value
## 5606.20 40 0
##
## Results for subgroups (fixed effect model):
## k proportion 95%-CI Q I^2
## byvar = 10 7 0.1254 [0.1213; 0.1297] 341.42 98.2%
## byvar = 11 5 0.1613 [0.1555; 0.1673] 245.25 98.4%
## byvar = 12 7 0.1725 [0.1674; 0.1776] 347.29 98.3%
## byvar = 9 22 0.0554 [0.0537; 0.0571] 1460.40 98.6%
##
## Test for subgroup differences (fixed effect model):
## Q d.f. p-value
## Between groups 3211.84 3 0
## Within groups 2394.35 37 0
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau
## byvar = 10 7 0.1172 [0.0902; 0.1511] 0.1520 0.3898
## byvar = 11 5 0.1558 [0.1147; 0.2081] 0.1596 0.3995
## byvar = 12 7 0.1699 [0.1344; 0.2125] 0.1361 0.3690
## byvar = 9 22 0.0386 [0.0293; 0.0506] 0.4474 0.6689
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 76.95 3 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_LT, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b## proportion 95%-CI %W(fixed) %W(random)
## Florida Health,FYTS 0.0086 [0.0049; 0.0139] 0.1 2.9
## Florida Health,FYTS 0.0232 [0.0170; 0.0309] 0.4 3.2
## Florida Health,FYTS 0.0365 [0.0284; 0.0460] 0.5 3.3
## Florida Health,FYTS 0.0567 [0.0460; 0.0689] 0.7 3.3
## Florida Health,FYTS 0.0589 [0.0477; 0.0718] 0.7 3.3
## Florida Health,FYTS 0.1066 [0.0912; 0.1236] 1.2 3.4
## Florida Health,FYTS 0.1295 [0.1103; 0.1507] 1.1 3.4
## Florida Health,FYTS 0.0105 [0.0086; 0.0127] 0.9 3.4
## Florida Health,FYTS 0.0212 [0.0184; 0.0243] 1.7 3.4
## Florida Health,FYTS 0.0415 [0.0377; 0.0456] 3.4 3.4
## Florida Health,FYTS 0.0757 [0.0702; 0.0816] 5.0 3.4
## Florida Health,FYTS 0.0962 [0.0896; 0.1030] 5.5 3.4
## Florida Health,FYTS 0.1250 [0.1172; 0.1332] 6.1 3.4
## Florida Health,FYTS 0.1459 [0.1366; 0.1557] 5.6 3.4
## NIH, PATH 0.0085 [0.0028; 0.0197] 0.0 2.1
## NIH, PATH 0.0162 [0.0108; 0.0231] 0.2 3.1
## NIH, PATH 0.0369 [0.0296; 0.0454] 0.7 3.3
## NIH, PATH 0.0658 [0.0558; 0.0770] 1.1 3.4
## NIH, PATH 0.1140 [0.1013; 0.1277] 2.0 3.4
## NIH, PATH 0.1445 [0.1301; 0.1599] 2.3 3.4
## NIH, PATH 0.1796 [0.1638; 0.1963] 2.7 3.4
## Miech (2020),MTF 0.0399 [0.0346; 0.0458] 1.6 3.4
## Miech (2020),MTF 0.0980 [0.0894; 0.1071] 3.3 3.4
## Miech (2020),MTF 0.1189 [0.1092; 0.1292] 3.6 3.4
## Miech (2020),MTF 0.0550 [0.0485; 0.0621] 2.0 3.4
## Miech (2020),MTF 0.1420 [0.1321; 0.1523] 4.8 3.4
## Miech (2020),MTF 0.1559 [0.1452; 0.1671] 4.7 3.4
## Dai (2020),NYTS 0.1110 [0.1064; 0.1157] 14.8 3.5
## Dai (2020),NYTS 0.1470 [0.1422; 0.1520] 21.2 3.5
## Kowitt (2019),NCYTS 0.0959 [0.0853; 0.1074] 2.1 3.4
##
## Number of studies combined: k = 30
##
## proportion 95%-CI
## Fixed effect model 0.1061 [0.1044; 0.1078]
## Random effects model 0.0652 [0.0537; 0.0790]
##
## Quantifying heterogeneity:
## tau^2 = 0.3211 [0.3197; 1.0672]; tau = 0.5667 [0.5654; 1.0330]
## I^2 = 99.2% [99.1%; 99.3%]; H = 11.00 [10.34; 11.70]
##
## Test of heterogeneity:
## Q d.f. p-value
## 3510.47 29 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studiesforest(b,digits = 3,transf= transf.ilogit.int)
Prevalence by year 2019-20
Meta-analysis
## proportion 95%-CI %W(fixed) %W(random) byvar
## Florida Health,FYTS 0.0152 [0.0099; 0.0224] 0.2 3.9 9
## Florida Health,FYTS 0.0343 [0.0261; 0.0443] 0.5 4.1 9
## Florida Health,FYTS 0.0663 [0.0551; 0.0788] 1.0 4.1 9
## Florida Health,FYTS 0.1062 [0.0922; 0.1216] 1.5 4.2 9
## Florida Health,FYTS 0.1349 [0.1176; 0.1538] 1.5 4.2 10
## Florida Health,FYTS 0.1646 [0.1454; 0.1853] 1.7 4.2 11
## Florida Health,FYTS 0.2004 [0.1772; 0.2251] 1.6 4.2 12
## Florida Health,FYTS 0.0363 [0.0311; 0.0421] 1.5 4.2 9
## Florida Health,FYTS 0.0824 [0.0748; 0.0904] 3.4 4.2 9
## Florida Health,FYTS 0.1560 [0.1462; 0.1661] 6.2 4.2 9
## Florida Health,FYTS 0.2289 [0.2167; 0.2415] 7.2 4.2 9
## Florida Health,FYTS 0.3114 [0.2976; 0.3254] 8.5 4.2 10
## Florida Health,FYTS 0.3483 [0.3330; 0.3640] 7.6 4.2 11
## Florida Health,FYTS 0.3268 [0.3106; 0.3433] 6.4 4.2 12
## CDC, NYTS 0.0256 [0.0196; 0.0328] 0.5 4.1 9
## CDC, NYTS 0.0555 [0.0465; 0.0655] 1.1 4.1 9
## CDC, NYTS 0.0931 [0.0816; 0.1056] 1.8 4.2 9
## CDC, NYTS 0.1790 [0.1623; 0.1967] 2.6 4.2 9
## CDC, NYTS 0.2787 [0.2585; 0.2996] 3.4 4.2 10
## CDC, NYTS 0.3200 [0.2984; 0.3421] 3.5 4.2 11
## CDC, NYTS 0.3798 [0.3573; 0.4027] 3.9 4.2 12
## Miech (2020),MTF 0.0900 [0.0841; 0.0962] 6.5 4.2 9
## Miech (2020),MTF 0.2180 [0.2096; 0.2267] 14.1 4.2 10
## Miech (2020),MTF 0.2371 [0.2280; 0.2463] 13.8 4.2 12
##
## Number of studies combined: k = 24
##
## proportion 95%-CI
## Fixed effect model 0.2064 [0.2034; 0.2095]
## Random effects model 0.1339 [0.1048; 0.1696]
##
## Quantifying heterogeneity:
## tau^2 = 0.4767 [0.4129; 1.5129]; tau = 0.6904 [0.6426; 1.2300]
## I^2 = 99.5% [99.5%; 99.6%]; H = 14.54 [13.74; 15.40]
##
## Test of heterogeneity:
## Q d.f. p-value
## 4864.62 23 0
##
## Results for subgroups (fixed effect model):
## k proportion 95%-CI Q I^2
## byvar = 10 4 0.2461 [0.2395; 0.2528] 238.57 98.7%
## byvar = 11 3 0.3111 [0.3000; 0.3223] 154.88 98.7%
## byvar = 12 4 0.2751 [0.2678; 0.2825] 231.19 98.7%
## byvar = 9 13 0.1168 [0.1135; 0.1201] 1536.45 99.2%
##
## Test for subgroup differences (fixed effect model):
## Q d.f. p-value
## Between groups 2703.53 3 0
## Within groups 2161.09 20 0
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau
## byvar = 10 4 0.2289 [0.1730; 0.2964] 0.1250 0.3536
## byvar = 11 3 0.2694 [0.1825; 0.3785] 0.1936 0.4400
## byvar = 12 4 0.2811 [0.2149; 0.3583] 0.1297 0.3602
## byvar = 9 13 0.0718 [0.0503; 0.1013] 0.4733 0.6880
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 45.95 3 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_LT, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b## proportion 95%-CI %W(fixed) %W(random)
## Florida Health,FYTS 0.0152 [0.0099; 0.0224] 0.2 3.7
## Florida Health,FYTS 0.0343 [0.0261; 0.0443] 0.4 3.9
## Florida Health,FYTS 0.0663 [0.0551; 0.0788] 0.8 4.0
## Florida Health,FYTS 0.1062 [0.0922; 0.1216] 1.3 4.0
## Florida Health,FYTS 0.1349 [0.1176; 0.1538] 1.3 4.0
## Florida Health,FYTS 0.1646 [0.1454; 0.1853] 1.4 4.0
## Florida Health,FYTS 0.2004 [0.1772; 0.2251] 1.4 4.0
## Florida Health,FYTS 0.0363 [0.0311; 0.0421] 1.2 4.0
## Florida Health,FYTS 0.0824 [0.0748; 0.0904] 2.8 4.0
## Florida Health,FYTS 0.1560 [0.1462; 0.1661] 5.2 4.0
## Florida Health,FYTS 0.2289 [0.2167; 0.2415] 6.0 4.0
## Florida Health,FYTS 0.3114 [0.2976; 0.3254] 7.1 4.0
## Florida Health,FYTS 0.3483 [0.3330; 0.3640] 6.4 4.0
## Florida Health,FYTS 0.3268 [0.3106; 0.3433] 5.4 4.0
## CDC, NYTS 0.0256 [0.0196; 0.0328] 0.4 3.9
## CDC, NYTS 0.0555 [0.0465; 0.0655] 0.9 4.0
## CDC, NYTS 0.0931 [0.0816; 0.1056] 1.5 4.0
## CDC, NYTS 0.1790 [0.1623; 0.1967] 2.2 4.0
## CDC, NYTS 0.2787 [0.2585; 0.2996] 2.9 4.0
## CDC, NYTS 0.3200 [0.2984; 0.3421] 3.0 4.0
## CDC, NYTS 0.3798 [0.3573; 0.4027] 3.2 4.0
## CDC, NYTS 0.1756 [0.1695; 0.1819] 16.0 4.1
## Miech (2020),MTF 0.0900 [0.0841; 0.0962] 5.5 4.0
## Miech (2020),MTF 0.2180 [0.2096; 0.2267] 11.8 4.1
## Miech (2020),MTF 0.2371 [0.2280; 0.2463] 11.6 4.1
##
## Number of studies combined: k = 25
##
## proportion 95%-CI
## Fixed effect model 0.2013 [0.1985; 0.2040]
## Random effects model 0.1357 [0.1087; 0.1680]
##
## Quantifying heterogeneity:
## tau^2 = 0.4076 [0.3767; 1.3608]; tau = 0.6384 [0.6137; 1.1665]
## I^2 = 99.5% [99.5%; 99.6%]; H = 14.34 [13.55; 15.17]
##
## Test of heterogeneity:
## Q d.f. p-value
## 4934.75 24 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studiesforest(b,digits = 3,transf= transf.ilogit.int)
(REGROUP) 12-m National + State surveys
Prevalence by year 2017-18
Meta-analysis
## proportion 95%-CI %W(fixed) %W(random) byvar
## Miech (2020),MTF 0.0299 [0.0253; 0.0351] 7.1 16.5 9
## Miech (2020),MTF 0.0810 [0.0731; 0.0894] 16.4 16.7 10
## Miech (2020),MTF 0.0950 [0.0862; 0.1045] 17.5 16.7 12
## Miech (2020),MTF 0.0441 [0.0382; 0.0505] 9.4 16.6 9
## Miech (2020),MTF 0.1240 [0.1146; 0.1337] 25.4 16.8 10
## Miech (2020),MTF 0.1310 [0.1210; 0.1415] 24.2 16.8 12
##
## Number of studies combined: k = 6
##
## proportion 95%-CI
## Fixed effect model 0.0925 [0.0889; 0.0962]
## Random effects model 0.0751 [0.0503; 0.1106]
##
## Quantifying heterogeneity:
## tau^2 = 0.2803 [0.1298; 2.1247]; tau = 0.5295 [0.3603; 1.4576]
## I^2 = 98.9% [98.5%; 99.2%]; H = 9.56 [8.12; 11.24]
##
## Test of heterogeneity:
## Q d.f. p-value
## 456.77 5 < 0.0001
##
## Results for subgroups (fixed effect model):
## k proportion 95%-CI Q I^2
## byvar = 10 2 0.1051 [0.0989; 0.1116] 44.82 97.8%
## byvar = 12 2 0.1147 [0.1079; 0.1217] 26.50 96.2%
## byvar = 9 2 0.0373 [0.0337; 0.0414] 13.02 92.3%
##
## Test for subgroup differences (fixed effect model):
## Q d.f. p-value
## Between groups 372.42 2 < 0.0001
## Within groups 84.35 3 < 0.0001
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau
## byvar = 10 2 0.1005 [0.0656; 0.1510] 0.1098 0.3313
## byvar = 12 2 0.1119 [0.0812; 0.1521] 0.0628 0.2505
## byvar = 9 2 0.0364 [0.0249; 0.0530] 0.0744 0.2727
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 22.25 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
## proportion 95%-CI %W(fixed) %W(random)
## Miech (2020),MTF 0.0299 [0.0253; 0.0351] 3.2 14.1
## Miech (2020),MTF 0.0810 [0.0731; 0.0894] 7.4 14.3
## Miech (2020),MTF 0.0950 [0.0862; 0.1045] 7.8 14.3
## Miech (2020),MTF 0.0441 [0.0382; 0.0505] 4.2 14.2
## Miech (2020),MTF 0.1240 [0.1146; 0.1337] 11.4 14.4
## Miech (2020),MTF 0.1310 [0.1210; 0.1415] 10.8 14.4
## Doggett (2020),COMPASS 0.0572 [0.0551; 0.0593] 55.2 14.4
##
## Number of studies combined: k = 7
##
## proportion 95%-CI
## Fixed effect model 0.0711 [0.0692; 0.0730]
## Random effects model 0.0723 [0.0507; 0.1019]
##
## Quantifying heterogeneity:
## tau^2 = 0.2554 [0.1087; 1.4685]; tau = 0.5054 [0.3297; 1.2118]
## I^2 = 99.2% [99.0%; 99.4%]; H = 11.21 [9.81; 12.81]
##
## Test of heterogeneity:
## Q d.f. p-value
## 754.30 6 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
Prevalence by year 2019-20
Meta-analysis
## proportion 95%-CI %W(fixed) %W(random) byvar
## Miech (2020),MTF 0.0700 [0.0647; 0.0756] 16.9 33.3 9
## Miech (2020),MTF 0.1940 [0.1858; 0.2023] 42.2 33.4 10
## Miech (2020),MTF 0.2080 [0.1993; 0.2169] 40.9 33.4 12
##
## Number of studies combined: k = 3
##
## proportion 95%-CI
## Fixed effect model 0.1700 [0.1653; 0.1749]
## Random effects model 0.1441 [0.0809; 0.2436]
##
## Quantifying heterogeneity:
## tau^2 = 0.3277 [0.1089; 16.3914]; tau = 0.5725 [0.3301; 4.0486]
## I^2 = 99.7% [99.6%; 99.8%]; H = 18.56 [15.79; 21.82]
##
## Test of heterogeneity:
## Q d.f. p-value
## 689.06 2 < 0.0001
##
## Results for subgroups (fixed effect model):
## k proportion 95%-CI Q I^2
## byvar = 10 1 0.1940 [0.1859; 0.2022] 0.00 --
## byvar = 12 1 0.2080 [0.1994; 0.2169] 0.00 --
## byvar = 9 1 0.0700 [0.0648; 0.0755] 0.00 --
##
## Test for subgroup differences (fixed effect model):
## Q d.f. p-value
## Between groups 689.06 2 < 0.0001
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau
## byvar = 10 1 0.1940 [0.1859; 0.2022] -- --
## byvar = 12 1 0.2080 [0.1994; 0.2169] -- --
## byvar = 9 1 0.0700 [0.0648; 0.0755] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 689.06 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_12m, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b## proportion 95%-CI %W(fixed) %W(random)
## Miech (2020),MTF 0.0700 [0.0647; 0.0756] 16.9 33.3
## Miech (2020),MTF 0.1940 [0.1858; 0.2023] 42.2 33.4
## Miech (2020),MTF 0.2080 [0.1993; 0.2169] 40.9 33.4
##
## Number of studies combined: k = 3
##
## proportion 95%-CI
## Fixed effect model 0.1700 [0.1653; 0.1749]
## Random effects model 0.1441 [0.0809; 0.2436]
##
## Quantifying heterogeneity:
## tau^2 = 0.3277 [0.1089; 16.3914]; tau = 0.5725 [0.3301; 4.0486]
## I^2 = 99.7% [99.6%; 99.8%]; H = 18.56 [15.79; 21.82]
##
## Test of heterogeneity:
## Q d.f. p-value
## 689.06 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studiesforest(b,digits = 3,transf= transf.ilogit.int)
(REGROUP) 30d National + State surveys
Prevalence by year 2013-16
Meta-analysis
## proportion 95%-CI %W(fixed) %W(random) byvar
## Johnson (2016),HKCS 0.0050 [0.0029; 0.0080] 11.3 23.3 9
## Johnson (2016),HKCS 0.0098 [0.0067; 0.0138] 21.2 25.0 10
## Johnson (2016),HKCS 0.0129 [0.0093; 0.0174] 27.7 25.6 11
## Johnson (2016),HKCS 0.0234 [0.0180; 0.0300] 39.8 26.1 12
##
## Number of studies combined: k = 4
##
## proportion 95%-CI
## Fixed effect model 0.0139 [0.0119; 0.0163]
## Random effects model 0.0113 [0.0062; 0.0204]
##
## Quantifying heterogeneity:
## tau^2 = 0.3401 [0.0963; 5.5794]; tau = 0.5832 [0.3104; 2.3621]
## I^2 = 92.3% [83.5%; 96.4%]; H = 3.60 [2.46; 5.28]
##
## Test of heterogeneity:
## Q d.f. p-value
## 38.98 3 < 0.0001
##
## Results for subgroups (fixed effect model):
## k proportion 95%-CI Q I^2
## byvar = 10 1 0.0098 [0.0069; 0.0138] 0.00 --
## byvar = 11 1 0.0129 [0.0095; 0.0174] 0.00 --
## byvar = 12 1 0.0234 [0.0183; 0.0300] 0.00 --
## byvar = 9 1 0.0050 [0.0031; 0.0080] 0.00 --
##
## Test for subgroup differences (fixed effect model):
## Q d.f. p-value
## Between groups 38.98 3 < 0.0001
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau
## byvar = 10 1 0.0098 [0.0069; 0.0138] -- --
## byvar = 11 1 0.0129 [0.0095; 0.0174] -- --
## byvar = 12 1 0.0234 [0.0183; 0.0300] -- --
## byvar = 9 1 0.0050 [0.0031; 0.0080] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 38.98 3 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_30d, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b## proportion 95%-CI %W(fixed) %W(random)
## CDPHE,HKCS 0.0440 [0.0363; 0.0527] 25.3 14.7
## Barrington-Trimis (2020), 0.0060 [0.0034; 0.0097] 3.8 13.6
## Johnson (2016),HKCS 0.0050 [0.0029; 0.0080] 4.0 13.7
## Johnson (2016),HKCS 0.0098 [0.0067; 0.0138] 7.5 14.2
## Johnson (2016),HKCS 0.0129 [0.0093; 0.0174] 9.9 14.4
## Johnson (2016),HKCS 0.0234 [0.0180; 0.0300] 14.2 14.5
## Peters (2018),HHS 0.0491 [0.0418; 0.0572] 35.3 14.8
##
## Number of studies combined: k = 7
##
## proportion 95%-CI
## Fixed effect model 0.0283 [0.0258; 0.0310]
## Random effects model 0.0157 [0.0084; 0.0290]
##
## Quantifying heterogeneity:
## tau^2 = 0.6857 [0.3145; 4.3052]; tau = 0.8281 [0.5608; 2.0749]
## I^2 = 97.4% [96.1%; 98.2%]; H = 6.17 [5.07; 7.52]
##
## Test of heterogeneity:
## Q d.f. p-value
## 228.55 6 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studiesforest(b,digits = 3,transf= transf.ilogit.int)
Prevalence by year 2017-18
Meta-analysis
## proportion 95%-CI %W(fixed) %W(random) byvar
## Miech (2020),MTF 0.0160 [0.0127; 0.0200] 6.6 16.3 9
## Miech (2020),MTF 0.0431 [0.0373; 0.0495] 15.6 16.7 10
## Miech (2020),MTF 0.0501 [0.0436; 0.0572] 16.6 16.7 12
## Miech (2020),MTF 0.0260 [0.0215; 0.0311] 9.7 16.5 9
## Miech (2020),MTF 0.0701 [0.0629; 0.0778] 26.1 16.9 10
## Miech (2020),MTF 0.0751 [0.0674; 0.0835] 25.3 16.9 12
##
## Number of studies combined: k = 6
##
## proportion 95%-CI
## Fixed effect model 0.0517 [0.0490; 0.0546]
## Random effects model 0.0415 [0.0277; 0.0618]
##
## Quantifying heterogeneity:
## tau^2 = 0.2683 [0.1219; 2.0404]; tau = 0.5180 [0.3492; 1.4284]
## I^2 = 98.0% [97.1%; 98.7%]; H = 7.15 [5.87; 8.70]
##
## Test of heterogeneity:
## Q d.f. p-value
## 255.49 5 < 0.0001
##
## Results for subgroups (fixed effect model):
## k proportion 95%-CI Q I^2
## byvar = 10 2 0.0585 [0.0538; 0.0636] 30.25 96.7%
## byvar = 12 2 0.0640 [0.0589; 0.0696] 21.82 95.4%
## byvar = 9 2 0.0214 [0.0186; 0.0246] 11.14 91.0%
##
## Test for subgroup differences (fixed effect model):
## Q d.f. p-value
## Between groups 192.28 2 < 0.0001
## Within groups 63.21 3 < 0.0001
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau
## byvar = 10 2 0.0551 [0.0340; 0.0882] 0.1285 0.3585
## byvar = 12 2 0.0615 [0.0412; 0.0910] 0.0890 0.2983
## byvar = 9 2 0.0205 [0.0127; 0.0328] 0.1109 0.3330
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 13.67 2 0.0011
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_30d, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b## proportion 95%-CI %W(fixed) %W(random)
## CDPHE,HKCS 0.0400 [0.0342; 0.0465] 9.9 12.5
## Miech (2020),MTF 0.0160 [0.0127; 0.0200] 4.8 12.2
## Miech (2020),MTF 0.0431 [0.0373; 0.0495] 11.5 12.5
## Miech (2020),MTF 0.0501 [0.0436; 0.0572] 12.2 12.5
## Miech (2020),MTF 0.0260 [0.0215; 0.0311] 7.1 12.4
## Miech (2020),MTF 0.0701 [0.0629; 0.0778] 19.2 12.6
## Miech (2020),MTF 0.0751 [0.0674; 0.0835] 18.6 12.6
## Nguyen (2019),HHS 0.0967 [0.0864; 0.1079] 16.6 12.6
##
## Number of studies combined: k = 8
##
## proportion 95%-CI
## Fixed effect model 0.0561 [0.0535; 0.0588]
## Random effects model 0.0461 [0.0324; 0.0650]
##
## Quantifying heterogeneity:
## tau^2 = 0.2714 [0.1367; 1.3832]; tau = 0.5209 [0.3697; 1.1761]
## I^2 = 98.1% [97.4%; 98.7%]; H = 7.32 [6.21; 8.62]
##
## Test of heterogeneity:
## Q d.f. p-value
## 374.94 7 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studiesforest(b,digits = 3,transf= transf.ilogit.int)
Prevalence by year 2019-20
Meta-analysis
## proportion 95%-CI %W(fixed) %W(random) byvar
## Miech (2020),MTF 0.0390 [0.0350; 0.0432] 14.0 33.2 9
## Miech (2020),MTF 0.1260 [0.1192; 0.1330] 42.8 33.4 10
## Miech (2020),MTF 0.1400 [0.1326; 0.1476] 43.1 33.4 12
##
## Number of studies combined: k = 3
##
## proportion 95%-CI
## Fixed effect model 0.1128 [0.1088; 0.1169]
## Random effects model 0.0897 [0.0478; 0.1620]
##
## Quantifying heterogeneity:
## tau^2 = 0.3530 [0.1253; 19.1725]; tau = 0.5942 [0.3540; 4.3786]
## I^2 = 99.6% [99.4%; 99.7%]; H = 15.85 [13.23; 18.99]
##
## Test of heterogeneity:
## Q d.f. p-value
## 502.42 2 < 0.0001
##
## Results for subgroups (fixed effect model):
## k proportion 95%-CI Q I^2
## byvar = 10 1 0.1260 [0.1193; 0.1330] 0.00 --
## byvar = 12 1 0.1400 [0.1327; 0.1476] 0.00 --
## byvar = 9 1 0.0390 [0.0351; 0.0432] 0.00 --
##
## Test for subgroup differences (fixed effect model):
## Q d.f. p-value
## Between groups 502.42 2 < 0.0001
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau
## byvar = 10 1 0.1260 [0.1193; 0.1330] -- --
## byvar = 12 1 0.1400 [0.1327; 0.1476] -- --
## byvar = 9 1 0.0390 [0.0351; 0.0432] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 502.42 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_30d, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b## proportion 95%-CI %W(fixed) %W(random)
## CDPHE,HKCS 0.0680 [0.0612; 0.0754] 12.0 24.9
## Miech (2020),MTF 0.0390 [0.0350; 0.0432] 12.4 24.9
## Miech (2020),MTF 0.1260 [0.1192; 0.1330] 37.7 25.1
## Miech (2020),MTF 0.1400 [0.1326; 0.1476] 38.0 25.1
##
## Number of studies combined: k = 4
##
## proportion 95%-CI
## Fixed effect model 0.1063 [0.1027; 0.1100]
## Random effects model 0.0838 [0.0496; 0.1380]
##
## Quantifying heterogeneity:
## tau^2 = 0.3244 [0.1194; 5.5102]; tau = 0.5696 [0.3455; 2.3474]
## I^2 = 99.5% [99.3%; 99.6%]; H = 14.00 [11.91; 16.45]
##
## Test of heterogeneity:
## Q d.f. p-value
## 587.82 3 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studiesforest(b,digits = 3,transf= transf.ilogit.int)